Machine Learning-Based Emotional Recognition in Surveillance Video Images in the Context of Smart City Safety

Abstract

The effective extraction of deep information from surveillance video lays the basis for smart city safety. However, the surveillance video images contain complex targets, whose expression changes are difficult to capture. The traditional face expression recognition methods or sentiment analysis algorithms have a poor application effect. Based on machine learning (ML), this paper explores the emotional recognition in surveillance video images in the context of smart city safety. Firstly, the potential textures of surveillance video images were extracted under multi-order double cross (MODC) mode, and the optical flow features of facial expressions were detected in these images. Next, a facial expression recognition model was constructed based on the DeepID convolutional neural network (CNN), and an emotional semantic space was established for the face images in surveillance video. The proposed method was proved effective through experiments. The research results provide a reference for emotional recognition in images of other fields.